2 citations · 2 across the 3 of their papers we have counts for
4 papers
QChunker: Learning Question-Aware Text Chunking for Domain RAG via Multi-Agent Debate
Jihao Zhao, Daixuan Li, Pengfei Li +3
The effectiveness upper bound of retrieval-augmented generation (RAG) is fundamentally constrained by the semantic integrity and information granularity of text chunks in its knowl…
A Survey of Reinforcement Learning for Large Reasoning Models
Kaiyan Zhang, Yuxin Zuo, Bingxiang He +36
In this paper, we survey recent advances in Reinforcement Learning (RL) for reasoning with Large Language Models (LLMs). RL has achieved remarkable success in advancing the frontie…
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
MiniMax, :, Aili Chen +125
We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combin…
One RL to See Them All: Visual Triple Unified Reinforcement Learning
Yan Ma, Linge Du, Xuyang Shen +7
Reinforcement learning (RL) is becoming an important direction for post-training vision-language models (VLMs), but public training methodologies for unified multimodal RL remain m…